999
Background—Microvascular renal and retinal diseases are common major complications of type 2 diabetes mellitus. The relation between plasma lipids and microvascular disease is not well established.
Methods and Results—The case subjects were 2535 patients with type 2 diabetes mellitus with an average duration of 14 years, 1891 of whom had kidney disease and 1218 with retinopathy. The case subjects were matched for diabetes mellitus duration, age, sex, and low-density lipoprotein cholesterol to 3683 control subjects with type 2 diabetes mellitus who did not have kidney disease or retinopathy. The study was conducted in 24 sites in 13 countries. The primary analysis included kidney disease and retinopathy cases. Matched analysis was performed by use of site-specific conditional logistic regression in multivariable models that adjusted for hemoglobin A1c, hypertension, and statin treatment. Mean low-density lipoprotein cholesterol concentration was 2.3 mmol/L. The microvascular disease odds ratio increased by a factor of 1.16 (95% confidence interval, 1.11–1.22) for every 0.5 mmol/L (≈1 quintile) increase in triglycerides or decreased by a factor of 0.92 (0.88–0.96) for every 0.2 mmol/L (≈1 quintile) increase in high-density lipoprotein cholesterol. For kidney disease, the odds ratio increased by 1.23 (1.16–1.31) with triglycerides and decreased by 0.86
© 2013 American Heart Association, Inc.
Circulation is available at http://circ.ahajournals.org DOI: 10.1161/CIRCULATIONAHA.113.002529
Received May 31, 2013; accepted December 4, 2013.
From the Nutrition Department, Harvard School of Public Health, Boston, MA (F.M.S., L.B.); Cliniques universitaires Saint Luc, Université catholique de Louvain, Brussels, Belgium (M.P.H.); Department of Medicine, University of Padova, Padova, Italy (P.F., I.B.); Department of Endocrinology Diabetology Nutrition, Jean Verdier Hospital, AP-HP, Le Centre de Recherché en Nutrition Humaine d’Ille de France, Paris Nord University, Bondy, France (P.V.); University of Western Australia, Crawley, Australia (T.D.); Harvard Medical School, Joslin Diabetes Center, Boston, MA (E.H., S.H.-B.); University of Würzburg, Würzburg, Germany (C.W.); College of Medicine, King Saud University, Riyadh, Kingdom of Saudi Arabia (K.A.-R.); LMC Diabetes and Endocrinology, Toronto, Ontario, Canada (R.A.); Department of Medicine, Section of Endocrinology, University of Verona, Verona, Italy (E.B., G.Z.); Ramathibodi Hospital, Bangkok, Thailand (P.B.); Department of Internal Medicine, National Taiwan University Hospital, Taipei, Taiwan (L.-M.C., Y.-D.J.); Rangsit School of Medicine, Rajavithi Hospital, Bangkok, Thailand (C.D.); North York General Hospital and LMC Diabetes and Endocrinology Centres, Toronto, Ontario, Canada (R.G.); Channing Division of Network Medicine, Brigham and Women’s Hospital, Boston, MA (B.H., N.L., V.J.C.); CIBERDEM and Vall d’Hebron Research Institute, Barcelona, Spain (C.H., R.S.); Keio University School of Medicine, Tokyo, Japan (H.I., M.T.); Shanghai Clinical Center for Diabetes, Shanghai Diabetes Institute, Shanghai Jiaotong University, Affiliated Sixth People’s Hospital, Shanghai, China (W.J.); Department of Diabetes and Metabolic Diseases, The University of Tokyo, Tokyo, Japan (T.K.); University of Toronto, Toronto, Ontario, Canada (L.L.); Tokyo Medical University, Tokyo, Japan (T.M., M.O.); Department of General Internal Medicine, National Cancer Center Hospital, Tokyo, Japan (K.O.); Tokyo Medical University Hachioji Medical Center, Tokyo, Japan (A.O.); Beijing 301 Military Hospital, Beijing, China (C.P.); Department of Endocrinology and Metabolism, Shanghai Clinical Center for Diabetes, Shanghai Jiao Tong University Affiliated Sixth People’s Hospital, Shanghai, China (J.P.); Universitat Autònoma de Barcelona, Hospital del Mar, Barcelona, Spain (J.P.-B.); Department of Internal Medicine, University Hospital Center Zagreb School of Medicine, University of Zagreb, Zagreb, Croatia (Z.R.); Section of Endocrinology, Department of Clinical Pathophysiology, University of Florence, Florence, Italy (C.M.R.); Department of Ophthalmology, University Hospital Sestre Milosrdnice, Zagreb, Croatia (E.T.-R.); and LMC Diabetes and Endocrinology, Oakville, Ontario, Canada (D.T.-B.).
Guest Editor for this article was Robert H. Eckel, MD.
The online-only Data Supplement is available with this article at http://circ.ahajournals.org/lookup/suppl/doi:10.1161/CIRCULATIONAHA. 113.002529/-/DC1.
Correspondence to Frank M. Sacks, MD, Harvard School of Public Health, 665 Huntington Ave, Boston, MA 02115. E-mail fsacks@hsph.harvard.edu
Lipoprotein Cholesterol and Microvascular Kidney Disease
and Retinopathy in Type 2 Diabetes Mellitus
A Global Case–Control Study in 13 Countries
Frank M. Sacks, MD; Michel P. Hermans, MD; Paola Fioretto, MD; Paul Valensi, MD;
Timothy Davis, MD; Edward Horton, MD; Christoph Wanner, MD; Khalid Al-Rubeaan, MD;
Ronnie Aronson, MD; Isabella Barzon, MD; Louise Bishop, BS, RD; Enzo Bonora, MD;
Pongamorn Bunnag, MD; Lee-Ming Chuang, MD; Chaicharn Deerochanawong, MD;
Ronald Goldenberg, MD; Benjamin Harshfield, BA; Cristina Hernández, MD;
Susan Herzlinger-Botein, MD; Hiroshi Itoh, MD; Weiping Jia, MD; Yi-Der Jiang, MD;
Takashi Kadowaki, MD; Nancy Laranjo, BA; Lawrence Leiter, MD; Takashi Miwa, MD;
Masato Odawara, MD; Ken Ohashi, MD; Atsushi Ohno, MD; Changyu Pan, MD;
Jiemin Pan, MD; Juan Pedro-Botet, MD; Zeljko Reiner, MD; Carlo Maria Rotella, MD;
Rafael Simo, MD; Masami Tanaka, MD; Eugenia Tedeschi-Reiner, MD;
David Twum-Barima, MD; Giacomo Zoppini, MD; Vincent J. Carey, PhD
D
iabetes mellitus is a major cause of microvascular dis-ease, which includes kidney disease and retinopathy and their ultimate consequences, end-stage renal disease and blind-ness.1–4 Hyperglycemia and hypertension are major risk factors for the development of microvascular disease.2,4 Intensive con-trol of blood glucose and blood pressure to, or even beyond, currently recommended targets may provide some additional benefits in the prevention of diabetic microvascular disease but is often impossible to achieve because of the associated risks of hypoglycemia or hypotension.5,6 Therefore, it is necessary to identify other targets and treatments to make progress in slow-ing the development of diabetic kidney disease and retinopathy.Clinical Perspective on p 1008
Most epidemiological studies have found an association between serum triglycerides and diabetic kidney disease, although less consistently for serum high-density lipoprotein cholesterol (HDL-C).7–21 Results diverged among studies on diabetic retinopathy, especially in multivariable analysis.4,22–33 In randomized, controlled trials, treatment of patients with type 2 diabetes mellitus with fenofibrate, a peroxisome proliferator– activated receptor-α agonist, reduced the rate of decline in renal function,25,34 reduced albuminuria, and reduced the requirement for laser treatment of retinopathy.5,25,34,35 However, it is not clear whether these beneficial effects were caused by improvements in triglycerides or HDL-C or by other biological effects of per-oxisome proliferator–activated receptor-α activation.
The objective of the present international study was to determine whether low HDL-C or elevated triglycerides lev-els are associated with diabetic kidney disease and retinopathy independent of established determinants of microvascular dis-ease in patients with type 2 diabetes mellitus with low-density lipoprotein cholesterol (LDL-C) ≤3.4 mmol/L (130 mg/dL).
Methods
The study used a case–control design in 24 sites in 13 countries. Sites were either hospitals or diabetes clinics. The study was approved by the institutional review boards of the coordinating center and each clinical site.
Population
Case and control subjects were individuals with type 2 diabetes melli-tus documented in the medical record, ≥40 years of age, with LDL-C ≤3.4 mmol/L.
Data were compiled from clinical charts. All consecutive charts of patients with type 2 diabetes mellitus who met the selection cri-teria were processed, and the required parameters were recorded. The methodology was piloted for feasibility assessment at the site in Brussels, Belgium, and included case subjects ascertained from 1990 to 2009 (median visit date May 2006). The chart review period was in 2008 to 2010 for all other sites.
Case Subjects
Case subjects were patients with visits for ≥1 recorded ocular or renal microvascular complication (kidney disease or retinopathy, the lat-ter also including diabetic macular edema). Patients with nondiabetic kidney disease were excluded unless they presented with a known diabetic microvascular complication. Nondiabetic kidney disease was determined by the site’s principal investigator from the medical records. The index visit for a case was a complication-related visit to which a lipid panel measured within 6 months could be associated. Control Subjects
Control subjects were patients with type 2 diabetes mellitus with documented evidence of not having microvascular complications of the kidney and eye as defined below.
Case Definitions Kidney Disease
Kidney disease was defined as either proteinuria >300 mg/L, albuminuria, or estimated glomerular filtration rate <60 mL·min−1·1.73 m−2. Albuminuria was defined as either albu-min/creatinine ratio ≥30 μg/mg measured in a single morning urine sample, >20 μg/min in timed overnight urine collections, or >30 mg/24 h in a 24-hour urine collection. Glomerular fil-tration rate was estimated by the Modification of Diet in Renal Disease formula.
Retinopathy
Retinopathy was defined as laser treatment for diabetic retinopathy; Early Treatment Diabetic Retinopathy Study (EDTRS) staging ≥20 shown by fundus photography; Diabetic Retinopathy Disease Severity Scale 3, 4, or 5 shown by dilated ophthalmoscopy36; or maculopathy defined as moderate or severe with the Diabetic Macular Edema Disease Severity Scale,36 determined by dilated ophthalmoscopy with slit lamp or by biomicroscopy.
Measurements
All data were obtained from reviews of medical records. Fasting samples were obtained from 73% of case subjects and 76% of control subjects. Nonfasting samples were obtained in 10% of case subjects and 8% of control subjects. The remainder, 17% of case subjects and 16% of control subjects, lacked the information. Total cholesterol, HDL-C, LDL-C (measured directly or calculated), and triglycerides were assayed within the 6 months before the date of the index visit. Data obtained included age, sex, duration of diabetes mellitus, body weight, height, ethnicity, history of hypertension, blood pressure, current medical treatment, medications, smoking, cardiovascular dis-eases, fasting blood glucose, and hemoglobin A1c (HbA1c).
Quality Control
Quality control visits by the Data Coordinating Center team were conducted at 17 of the 24 sites. CMIC, a contract research organiza-tion in Japan, monitored the 3 Japanese sites. On-site single entry of data (with programmatic constraints to prevent out-of-range values (0.82–0.91) with high-density lipoprotein cholesterol. Retinopathy was associated with triglycerides and high-density lipoprotein cholesterol in matched analysis but not significantly after additional adjustment.
Conclusions—Diabetic kidney disease is associated worldwide with higher levels of plasma triglycerides and lower levels of high-density lipoprotein cholesterol among patients with good control of low-density lipoprotein cholesterol. Retinopathy was less robustly associated with these lipids. These results strengthen the rationale for studying dyslipidemia treatment to prevent diabetic microvascular disease. (Circulation. 2014;129:999-1008.)
and to minimize missing data) was performed with data management software developed for this project.
A 5% random sample of medical charts corresponding to digital study data were requested for review by the data coordinating center; 21 of the study sites complied with the request. Values for partici-pant age, sex, race, LDL-C, triglycerides, HDL-C, and outcome sta-tus were checked for concordance between the medical records and the study database. The overall discordance rate was 1.2%. Precision and accuracy of laboratories used by participating sites were assessed (online-only Data Supplement).
Sample Size Determination
Prevalence and incidence statistics derived from the PROCAM study (G. Assmann, written communication, April 8, 2008) were used to obtain a target sample size of 100 case subjects in each clinical site, matched 1:1 with control subjects, which would provide 80% power to identify a relative risk of microvascular complications of 2.0 in any site, assuming a 30% prevalence of dyslipidemia among control subjects.37,38 Collection of data on additional control subjects, when
available, was allowed as a means of increasing power. Data Analysis
The primary outcome was a diagnosis of kidney disease, retinopathy, or both, according to the protocol. Kidney disease and retinopathy were analyzed as individual outcomes in secondary analyses that included sites that reported ≥10 cases of either event for each site. Multivariable data on all N1 case subjects and N2 control subjects was assembled into a distance matrix of dimension (N1+N2)×(N1+N2). The optimal partitioning of the full data set into strata that included at least 1 case and 1 control per stratum was determined by use of the Hansen and Klopfer procedure39 (which in turn uses graph-theoretical
optimization procedures attributable to Bertsekas and Tseng); no con-trol was used in >1 stratum. The partitioning is optimal in the sense that no other partition has a smaller sum of within-stratum distances; that is, the groupings together of case and control subjects maximizes the similarities of case subjects to control subjects among all pos-sible groupings. Strata were required to be homogeneous in sex and were formed to minimize the sum of squared Mahalanobis distances over all possible groupings of case and control subjects within sites. Mahalanobis distance was computed based on values of LDL-C, number of years elapsed subsequent to diabetes mellitus diagnosis, and age.
Statistical modeling proceeded along 3 main axes. Case prediction models took several different forms. Most parsimoniously, quintiles of triglycerides and HDL-C were scored 0 to 4, and a single degree of freedom test for trend was used. Separate quintile effects were also estimated to assess adequacy of the linearity assumption. Third, tests of triglycerides and HDL-C effects were conducted marginally (unad-justed); adjusted for statin treatment, hypertension status, and quintile scored HbA1c; and with “mutual” adjustment, in which triglycerides and HDL-C effects were assessed simultaneously. For 1 site, Toronto (2), the hypertension covariate was unavailable, and models for that site excluded this variable.
Statistics were summarized across sites by use of a random effects meta-analysis methodology.40 Finally, several sensitivity analyses
were performed on subsamples of the case subjects and on cases with the 4 specific retinopathy definitions. Results are presented as odds ratio (95% confidence limits). For some subgroups, full covariate adjustment was infeasible owing to data sparseness, and adjustments were limited to feasible variables.
Results
Characteristics of Case and Control Subjects
A total of 2535 case subjects were reported and were matched, within sites and within strata defined by sex, to 3683 control subjects. A total of 2034 strata were formed by the optimal matching procedure. The most common structure for strata was a 1:1 match (1125 such strata were formed), and 92% of strata consisted of 1 or 2 case subjects matched to a group of
≤6 control subjects. Within sites, the median within-stratum age range was computed as a measure of departure from per-fect matching on age; its median over all sites was 4.25 years. For duration of diabetes mellitus, the median within-site departure over all sites was 3 years, and for LDL-C level, the median within-site departure over all sites was 0.23 mmol/L. Characteristics of case and control subjects are shown in the Table. The meta-analytic estimates of differences between case and control subjects (denoted ∆) in these characteristics in the matched analyses that were used in the meta-analysis to compute odds ratios (ORs) were very small and not clini-cally meaningful; for instance, difference in duration of dia-betes mellitus was 0.9 years. Information was not available on menopausal status of the women; however, the mean age of the case subjects who were women was 66 years, and 89% were ≥50 years of age.
Kidney disease was present in 1891 case subjects and reti-nopathy in 1218 case subjects. A total of 574 case subjects had both kidney and eye disease. For the secondary analysis of kidney disease or retinopathy, separately, sites were included that reported ≥10 case subjects. The kidney disease analysis included all 24 sites and 1891 case subjects, whereas the reti-nopathy analysis included 21 sites and 1202 case subjects. Characteristics of the case and control subjects are shown in the Table, and in each of the sites in Table I in the online-only Data Supplement. Characteristics of the case subjects with diabetic kidney disease and retinopathy were similar.
The primary analysis considered microvascular compli-cation case status, which consisted of a diagnosis of at least 1 of diabetic kidney disease, retinopathy, or maculopathy. Using linear scoring of triglyceride quintiles, the OR for a microvascular complication corresponding to a difference of 1 quintile (≈0.5 mmol/L) was 1.16 (95% confidence interval [CI], 1.11–1.22), including the covariates stated in Methods (Figure 1). Using categorical factors for triglyceride quintile membership, the OR comparing fifth to first quintiles was 1.76 (95% CI, 1.38–2.25). For linear scoring of HDL-C quintiles, the OR for a 1-quintile difference (≈0.2 mmol/L) was 0.92 (95% CI, 0.88–0.96; Figure 1). The OR comparing fifth to first quintiles of HDL-C was 0.73 (95% CI, 0.60–0.90). When the linear quintile scoring model was fit that included triglyceride and HDL-C simultaneously, the OR for a 1-quintile difference of triglyceride was estimated at 1.15 (95% CI, 1.09–1.22) and that for a 1-quintile difference in HDL-C was estimated at 0.96 (95% CI, 0.91–1.01). Site-to-site variability in triglycer-ide effects was not statistically significant (P=0.22), with 22 of 24 sites yielding OR estimates >1 in the marginal, linear scoring model. Site-to-site variability in the HDL-C effect was also not statistically significant (P=0.42), and 20 of 24 sites yielded OR estimates <1 for this association. Tests for hetero-geneity were generally not significant for any of the analyses (see Figure legends).
A planned secondary analysis was the evaluation of the associations of triglycerides and HDL-C with each of the 2 microvascular event types. The OR for a kidney disease com-plication corresponding to a difference of 1 quintile in triglyc-erides (≈0.5 mmol/L) was 1.23 (95% CI, 1.16–1.31; Figure 2); the OR estimates were >1.0 in 23 of the 24 sites. Using cat-egorical factors, the OR comparing fifth to first quintiles (2.8
versus 0.7 mmol/L) was 2.24 (95% CI, 1.78–2.83; Figure 3). The OR for a kidney disease complication corresponding to a 1-quintile difference in HDL-C was 0.86 (95% CI, 0.82–0.91; Figure 2); the OR was <1.0 in 23 of the 24 sites. Using cat-egorical factors for HDL-C, the OR comparing fifth to first quintiles was 0.58 (95% CI, 0.47–0.73; Figure 3). In models that mutually adjusted for triglycerides and HDL-C, the OR for kidney disease was 1.20 (95% CI, 1.13–1.28) for a quin-tile increase in triglycerides and 0.92 (95% CI, 0.87–0.97) for a quintile increase in HDL-C (Figure 4). Kidney disease case subjects could qualify on the basis of albuminuria or low glomerular filtration rate; their characteristics are shown in Tables II and III in the online-only Data Supplement, respec-tively. The kidney disease ORs for triglycerides and HDL-C, individually and in mutual adjustment, were significant for low glomerular filtration rate (n=522 case subjects) and for albuminuria (n=1517 case subjects; Figure 5; Figures I to VI in the online-only Data Supplement).
The analysis of retinopathy included 21 sites that submit-ted ≥10 retinopathy case subjects, and the total number of case subjects was 1202. The OR for a retinopathy compli-cation was 1.09 (95% CI, 1.02–1.16) per quintile of triglyc-erides and 0.93 (95% CI, 0.86–1.0) per quintile of HDL-C using models that controlled for the matching factors only. However, additional control for hypertension and HbA1c
weakened these associations, and they did not remain signifi-cant. For triglycerides, the OR was 1.04 (95% CI, 0.98–1.11), and for HDL-C it was 0.97 (95% CI, 0.90–1.05; Figure 6). The ratio of triglycerides to HDL-C also did not have a sig-nificant association with retinopathy (OR, 1.04 [95% CI, 0.98–1.11] per quintile).
The ORs (95% CIs) for retinopathy per quintile of plasma triglycerides or HDL-C, not mutually adjusted, were deter-mined according to the 4 definitions or conditions: (1) Maculopathy (n=169 case subjects): triglycerides 1.09 (0.95– 1.26); high-density lipoprotein (HDL-C) 0.97 (0.78–1.19); (2) laser surgery: triglycerides 1.03 (0.93–1.14), HDL-C 0.93 (0.82–1.06); (3) fundus photography EDTRS: triglycerides 1.04 (0.92–1.16), HDL-C 0.98 (0.82–1.17); and (4) dilated ophthalmoscopy Diabetic Retinopathy Disease Severity score: triglycerides 1.00 (0.92–1.08), HDL-C 1.01 (0.94–1.10). Therefore, the results were similar and not significant across these retinopathy outcomes.
We evaluated the possible influence of coexisting kidney disease status on ORs associated with retinopathy for triglyc-erides and HDL-C. There were 630 retinopathy case subjects who did not have kidney disease, which constituted 52% of the total retinopathy case subjects. The OR (95% CI) for retinopa-thy for triglyceride was 0.97 (0.89–1.04) for the subset com-pared with 1.04 (0.98–1.11) for the total group; for HDL-C
Table. Descriptive Statistics of Case and Control Subjects
Case Subjects (n=2535) Kidney Cases (n=1891) Eye Cases (n=1202) Control Subjects (n=3683) Case–Control Difference P (H0: ∆=0) Demographics Male, n (%) 1470 (58) 1153 (61) 649 (54) 2040 (55) … … White/European, % 44 44 41 51 … … Age, y, mean (SD) 65 (11) 65 (11) 64 (10) 62 (11) 0.6 <0.01
Diabetes mellitus duration, y, mean (SD)
14 (9) 14 (9) 16 (9) 10 (8) 0.9 <0.01
Lipids, mmol/L, mean (SD)
Total cholesterol 4.30 (0.92) 4.26 (0.84) 4.36 (1.02) 4.30 (0.83) 0.01* 0.03 LDL-C 2.31 (0.63) 2.28 (0.64) 2.34 (0.62) 2.35 (0.85) 0.0* 0.34 HDL-C 1.20 (0.44) 1.18 (0.46) 1.23 (0.40) 1.26 (0.45) −0.04* <0.01 Triglycerides 1.78 (1.14) 1.85 (1.17) 1.72 (1.18) 1.61 (1.20) 0.12* <0.01 Hemoglobin A1c, % 8.1% (1.7%) 8.0% (1.7%) 8.3% (1.8%) 7.6% (1.6%) 0.46 <0.01 Medication, n (%) Fibrate use 195 (7.7) 163 (8.6) 81 (6.7) 291 (7.9) 0.0† 0.69 Statin use 1326 (52) 1053 (56) 571 (48) 1880 (51) 0.01† 0.23 Anti-DM use 2385 (94) 1782 (94) 1146 (95) 3301 (90) … … Clinical condition, n (%) Nephropathy 1891 (75) 1891 (100) 566 (47) 0 … … Retinopathy 1218 (48) 574 (30) 1202 (100) 0 … … Hypertension 2076 (82) 1588 (84) 981 (82) 2427 (66) 0.15† <0.01
A total of 2535 case subjects were reported and were matched, within sites and within strata defined by sex, to 3683 control subjects. A total of 574 case subjects had both kidney and eye disease. For the secondary analysis of kidney disease or retinopathy, separately, sites were included that reported ≥10 case subjects. The kidney disease analysis included all 24 sites and 1891 case subjects, whereas the retinopathy analysis included 21 sites and 1202 case subjects. Difference (∆) denotes the meta-analytic estimate of the mean difference between case and control subjects on general parameters of the study, allowing random intercepts for matched strata. DM indicates diabetes mellitus; HDL-C, high-density lipoprotein cholesterol; and LDL-C, low-density lipoprotein cholesterol.
*Denotes ∆ estimated after log transformation.
levels, it was 1.03 (0.93–1.14) for the subset compared with 0.97 (0.90–1.05) for the total group.
Because HbA1c level was strongly associated with case status, the ORs for microvascular diseases, kidney or eye, were determined for those with HbA1c above or below the median, 7.4%. ORs for those with HbA1c above the median (mean HbA1c 9.0% for 1554 case subjects, 8.9% for 1659 control subjects) were 1.14 (1.07–1.22) for triglycerides and 0.96 (0.91–1.03) for HDL; for those below the median (mean HbA1c 6.6% for 901 case subjects and 2007 control subjects), they were 1.15 (1.07–1.24) for triglycerides and 0.87 (0.81– 0.93) for HDL. These models were not adjusted for HbA1c. Heterogeneity tests for these analyses were not significant.
There were 564 case subjects who had both kidney disease and retinopathy. The OR for case status of both kidney disease and retinopathy compared with 3318 control subjects was 1.16 (95% CI, 1.07–1.25) for a 1-SD increase in triglyceride and 0.88 (95% CI, 0.80–0.96) for a 1-SD increase in HDL-C, similar to those in the full group.
Adjustment for prevalent cardiovascular disease in the matched conditional logistic regression meta-analysis had lit-tle impact on key inferences. The OR (95% CI) for increasing adjacent quintiles of triglyceride was 1.23 (1.15–1.31), and for HDL-C, it was 0.87 (0.82–0.92).
Additional sensitivity analyses evaluating those whose blood sample was taken in the fasting state (n=1306 case
Figure 1. Odds ratio (OR) for diabetic
kidney disease or retinopathy associated with a quintile increase in blood triglycerides (TG; 0.5 mmol/L) or high-density lipoprotein cholesterol (HDL-C; 0.2 mmol/L). Controlled by matching for age, sex, duration of diabetes mellitus, and low-density lipoprotein cholesterol level and by inclusion of hypertension and hemoglobin A1c in the multiple variable model. Size of symbols is proportional to number of case subjects. Test for heterogeneity among sites: TG, P=0.22; HDL-C, P=0.42. CI indicates confidence interval; and REALIST, Residual Risk Lipids and Standard Therapies study.
Figure 2. Odds ratios (OR) for kidney
disease for triglycerides (TG; left) and
high-density lipoprotein cholesterol (HDL-C;
right), not mutually adjusted. Matching
variables, covariates, and symbols are described in the legend to Figure 1. The analysis included all 24 sites and 1891 case subjects. Test for heterogeneity among sites: TG, P=0.14; HDL-C,
P=0.58. CI indicates confidence interval;
and REALIST, Residual Risk Lipids and Standard Therapies study.
subjects for kidney, 803 for eye) showed an OR (95% CI) for a microvascular complication associated with triglycerides of 1.16 (1.09–1.24) compared with 1.16 (1.11–1.22) for the total group. For a microvascular complication associated with HDL-C, the OR was 0.91 (0.86–0.96) compared with 0.92 (0.88–0.96) for the total group.
Similarly, subgroup analysis of those who were not tak-ing statins or fibrates returned ORs that were the same (or approximately the same) as for the full sample. For kidney disease in the no lipid-treatment group, the OR for a 1-SD
change in triglycerides was 1.24 (1.14–1.34), and for HDL-C, it was 0.83 (0.77–0.90; n=704 case subjects and 1570 control subjects). For retinopathy in the no lipid-treatment group, the OR for triglycerides was 0.99 (0.90–1.10), and for HDL-C, it was 0.99 (0.87–1.12; n=549 case subjects and 1318 control subjects).
The associations of triglycerides and HDL-C with kidney disease or retinopathy were similar among the geographic regions and ethnicities. Eight European sites were prepon-derantly (74%) white, and 9 Asian sites were preponprepon-derantly
Figure 4. Odds ratio (OR) for kidney
disease for triglycerides and high-density lipoprotein cholesterol (HDL-C), mutually adjusted. Matching variables, covariates, and symbols are described in the legends to Figures 1 and 2. Test for heterogeneity among sites: TG, P=0.19; HDL-C,
P=0.64. CI indicates confidence interval;
and REALIST, Residual Risk Lipids and Standard Therapies study.
Figure 3. Quintile-specific odds ratios for diabetic kidney disease associated with levels of blood triglycerides (TG) or high-density
lipoprotein cholesterol (HDL-C). Matching variables and covariates are described in the legend to Figure 1. Left, TG and HDL-C not
(89%) nonwhite. The ORs for adjacent quintiles of triglycer-ide were 1.13 (95% CI, 1.03–1.23) for Europe and 1.21 (95% CI, 1.10–1.34) for Asia; for adjacent quintiles of HDL-C, the ORs were 0.89 (95% CI, 0.78–1.01) for Europe and 0.94 (95% CI, 0.86–1.02) for Asia.
Discussion
Diabetes mellitus is the major cause of renal failure and vision loss in adults.1–4 Current treatments are effective in reducing the risk of development and progression; however, the residual risk for these complications remains high.6,41,42 Because the prevalence of type 2 diabetes mellitus continues to increase worldwide, it is expected that its major complications, kidney
disease and retinopathy, will increase in parallel. New targets and treatments are urgently needed.
In the present study, triglycerides and HDL-C were sig-nificantly and independently associated with diabetic micro-vascular disease and specifically with kidney disease. The associations with retinopathy were not robust after adjustment for hypertension and HbA1c. These associations were similar in magnitude among the sites and among geographic regions.
The strengths of the present study include its global scope of inclusion of sites and consistency of findings, demonstrated by lack of heterogeneity in nearly all of the meta-analyses, which supports wide generalizability across regions and ethnicities. The matching procedure that equalized age, sex, duration of Figure 5. Odds ratio for kidney disease defined by either low estimated glomerular filtration rate (n=522 case subjects) or albuminuria
(n=1517 case subjects) according to triglycerides (TG) or high-density lipoprotein cholesterol (HDL-C), not mutually adjusted. Matching variables, covariates, and symbols are described in the legend to Figure 1. Test for heterogeneity among sites (linear trend) for low estimated glomerular filtration rate: TG, P=0.24; HDL-C, P=0.03. Test for heterogeneity among sites (linear trend) for albuminuria: TG,
P=0.04; HDL-C, P=0.65. Q1 through Q5 indicate quintiles 1 through 5.
Figure 6. Odds ratio (OR) for retinopathy
associated with a quintile increase in blood triglycerides (TG; left) or
high-density lipoprotein cholesterol (HDL-C;
right). Matching variables, covariates, and
symbols are described in Figure 1. TG and HDL-C not mutually adjusted. Sites were included that reported ≥10 case subjects. The retinopathy analysis included 21 sites and 1202 case subjects. Test for heterogeneity among sites: TG, P=0.30; HDL-C, P=0.08. CI indicates confidence interval; and REALIST, Residual Risk Lipids and Standard Therapies study.
diabetes mellitus, and LDL-C concentration reduced the prob-ability of confounding and reverse causation. The findings for total microvascular events and for kidney disease were robust after adjustment for hypertension and HbA1c, 2 major influ-ences on the occurrence of diabetic microvascular disease. The present study had a large number of case subjects with diabetic microvascular disease; this reduced the chance of a false-negative, which might have occurred in previous studies on HDL-C and microvascular disease.
Limitations of the present study are its cross-sectional design and the potential for reverse causation; however, a typi-cal example of reverse causation, more aggressive treatment of lipid levels in case subjects, would bias the results to the null, because treatment would decrease triglycerides and raise HDL-C in the case subjects, the opposite of the actual results. It appears likely that matching case and control subjects on LDL-C equalized lipid treatment. The finding that lipid treat-ments were similar in the 2 groups supports this interpretation. The lipid laboratories at each site were not standardized or calibrated, although the laboratory survey that we conducted did not find serious differences in accuracy among the sites that participated, and the precision measurements were excel-lent. In any case, it would be unlikely that measurement error could occur differentially between case and control subjects. Although the blood samples were not required to be fasting, most were fasting, the same in case and control subjects, and the results for a fasting subgroup were similar to those for the total group.
Most previous studies reported that high plasma triglycerides were associated with diabetic kidney disease,8,10,12–14,17,19–21,43 although several did not find an association.7,9,16 With regard to HDL-C, the published literature is less consistent, with some studies showing a significant association,8,16,17,19–21,43 and others not.7,9–12,14,18 Certainly, type 2 errors could account for negative studies. In combination with previous stud-ies, we consider the findings of the present study regarding both high triglycerides and low HDL-C, considered alone or together in multiple variable models with consistent direc-tion of ORs among nearly all the sites and mostly nonsig-nificant tests of heterogeneity, as compelling evidence that triglycerides and HDL-C are indeed strongly associated with diabetic kidney disease.
The associations for retinopathy of triglycerides and HDL-C were weaker than for kidney disease and less robust after adjustment for known risk factors. The retinopathy literature is mixed with regard to triglycerides, with some studies showing a significant association4,24,29,32 and others not18,23,27,31,33,44 or showing mixed findings.30 For HDL-C, none of the studies found a significant association with dia-betic retinopathy,4,18,23,24,26–28,30–33,44 with 1 exception.29 The large number of case subjects (1202) in the present study provided sensitivity to identify even a relatively weak asso-ciation between retinopathy and triglycerides and HDL-C if one existed. Measurement error for retinopathy determined by ophthalmoscope or even fundus photography that requires grading has the potential to reduce the strength of associa-tions with triglycerides and HDL-C. However, the results for the association between triglycerides and HDL-C and retinopathy were similar regardless of the specific diagnostic
criterion, including laser surgery. The results suggest that associations of triglycerides and HDL-C with retinopathy may be dependent on confounding by other risk factors for microvascular disease, specifically hypertension and HbA1c, as we found.
The integrity of the blood-retinal barrier protects the retina against potentially harmful effects of extravasation of plasma lipoproteins. Apolipoprotein B lipoproteins may damage retinal capillaries, leading to extravasation, and they are present in retinas of diabetic people in proportion to the severity of retinopathy.45 These mechanistic findings and the present results support the concept that mechanisms involved in regulation or dysregulation of intraretinal lipid transport might be potentially more important than plasma lipid levels in the pathogenesis of diabetic retinopathy.46 This may differ from the effects of lipoproteins on kidney disease, in which extravasation of lipoproteins may more readily occur than in the retina.
Finally, the present study is restricted to information in clinic and hospital records that contain only standard lipid measurements needed for clinical management. It is possible that specific lipoprotein subfractions could be involved in reti-nopathy, such as apolipoprotein C-III–containing low-density lipoproteins and HDL that are strongly associated with coro-nary events47,48 and adversely affect endothelial cells49 or oxi-dized lipoproteins.45
In conclusion, this global study of lipid risk factors for dia-betic microvascular disease provides strong evidence for inde-pendent associations for high triglycerides and low HDL-C. These associations apply to kidney disease. It is possible that retinopathy may also be affected by triglyceride or HDL-C levels, but the association appears weak. Nevertheless, larger populations and meta-analyses could be helpful to further investigate the relationship between retinopathy and dys-lipidemia. Current guidelines for lipid treatment give more emphasis than before to the use of triglycerides and HDL-C for treatment thresholds and targets for macrovascular dis-ease.50 In view of the large and growing health burden of renal failure in diabetes mellitus, these findings have considerable importance in support of the establishment of additional lipid targets other than LDL-C to benefit the diabetic population at high residual risk for microvascular disease despite current standards of care.
Acknowledgments
We acknowledge the contributions of Najlaa A. Ahmad, Roderick Hamsirani, Tariq Alshaikh, Faryal Mehmood, Moniza Amer, and Wael AlMistehi, King Saud University, Riyadh, Saudi Arabia; Angelo Avogaro, University of Padova, Padova, Italy; Gianluca Bardini, University of Florence, Florence, Italy; Danielle Bedard, Leslie Berndl, and Shelly Perry, St. Michael’s Hospital, Toronto, Ontario, Canada; Michelle England, Fremantle Hospital, Crawley, Australia; Juana A. Flores-Roux, Hospital Del Mar, Barcelona, Spain; Brooke Lamparello, Joslin Diabetes Center, Boston, MA; Lu Ju Ming and Jin Nan; Beijing 301 Military Hospital, Beijing, China; Ivan Pećin and Ognjen Zrinščak, University Hospital Center, Zagreb, Croatia; Karim Takbou, Hospital Jean Verdier, Bondy Cedex, France; Yasmin Sallay and Karri Venn, LMC Endocrinology Centres, Toronto and Oakville, Ontario, Canada; Chiara Tommasi, University of Verona, Verona, Italy; Yi-Ren Hueng, Tri-Service General Hospital, Taipei, Taiwan; and Lei Zhang, Jiaotong University, Shanghai, China. We
are grateful to all study personnel at each participating site for their dedication to this study.
Sources of Funding
This study was funded by an R3i (Residual Risk Reduction Institute) Foundation grant to Brigham and Women’s Hospital and Harvard School of Public Health and to the individual study sites. The sponsor provided logistical support to the sites and the coordinating center.
Disclosures
Dr Sacks was a board member of the Residual Risk Reduction Institute (R3i), the sponsor of this research, until April 12, 2012. In 2011, he was also paid by R3i for his research/product develop-ment services. This interest was reviewed by Brigham and Women’s Hospital and Partners HealthCare in accordance with their institu-tional policies. He has given expert testimony in patent litigation on the side of Abbott. Dr Hermans is a board member of R3i and was also paid by R3i for his research/product development services. Dr Wanner has received honoraria from Merck and Astellas. Dr Valensi has received honoraria from Abbott. Dr Bunnag has received hono-raria from Abbott. Dr Goldenberg has received speakers fees, con-sultant fees, and research fees from Abbott Laboratories, Merck, AstraZeneca, and Pfizer. Dr Leiter has received research funding from, has provided continuing medical education on behalf of, and/ or has acted as a consultant to Abbott, AstraZeneca, Boeringer, BMS, Eli Lilly, GSK, Merck, Novartis, Novo Nordisk, Roche, Sanofi, and Servier. Dr Simo has participated on advisory panels for Novartis, Novo Nordisk, Lilly, and Abbott and has received travel, honorarium, and research support from these companies. Dr Reiner has received honoraria from Abbott, Sanofi-Aventis, AstraZeneca, and MSD. Dr Carey provided and was compensated for consulting services to the R3I Foundation. The remaining authors report no conflicts.
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CLINICAL PeRSPeCTIve
Diabetes mellitus is the major cause of renal failure and vision loss in adults. Current treatments are effective in reducing the risk of development and progression; however, the residual risk for these complications remains high. In this global case– control study of lipid risk factors for diabetic microvascular disease in 13 countries, triglycerides and high-density lipopro-tein cholesterol were significantly and independently associated with diabetic microvascular disease and specifically with kidney disease. Case and control subjects were matched for low-density lipoprotein cholesterol, which averaged 2.3 mmol/L (89 mg/dL). The odds ratio for kidney disease increased by 23% for every 0.5 mmol/L (44 mg/dL) increase in triglycerides and decreased by 14% for every 0.2 mmol/L (8 mg/dL) increase in high-density lipoprotein cholesterol. The associations with retinopathy were not robust after adjustment for hypertension and hemoglobin A1c. These associations were consistent and similar in magnitude among the sites and among geographic regions (Europe, Asia, Middle East, and North America). In view of the large and growing health burden of renal failure in diabetes mellitus, these findings have considerable importance in support of additional lipid targets other than low-density lipoprotein cholesterol to benefit the diabetic population at high residual risk for microvascular disease despite current standards of care.
